{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"BatchNormalization.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vEfBhTLH6Z_5","executionInfo":{"status":"ok","timestamp":1612695138132,"user_tz":-60,"elapsed":25134,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"f13babe7-f640-4e9c-8be2-53fa25a294df"},"source":["# this mounts your Google Drive to the Colab VM.\n","from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)\n","\n","# enter the foldername in your Drive where you have saved the unzipped\n","# assignment folder, e.g. 'cs231n/assignments/assignment3/'\n","FOLDERNAME = 'cs231n/assignments/assignment2/'\n","assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n","\n","# now that we've mounted your Drive, this ensures that\n","# the Python interpreter of the Colab VM can load\n","# python files from within it.\n","import sys\n","sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))\n","\n","# this downloads the CIFAR-10 dataset to your Drive\n","# if it doesn't already exist.\n","%cd drive/My\\ Drive/$FOLDERNAME/cs231n/datasets/\n","!bash get_datasets.sh\n","%cd /content"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n","/content/drive/My Drive/cs231n/assignments/assignment2/cs231n/datasets\n","/content\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"tags":["pdf-title"],"id":"xuYXcqnD6aAB"},"source":["# Batch Normalization\n","One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n","One idea along these lines is batch normalization which was proposed by [1] in 2015.\n","\n","The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n","\n","The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n","\n","It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n","\n","[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n","Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"]},{"cell_type":"code","metadata":{"tags":["pdf-ignore"],"colab":{"base_uri":"https://localhost:8080/"},"id":"pTnZkadM6aAC","executionInfo":{"status":"ok","timestamp":1612695168938,"user_tz":-60,"elapsed":5792,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"2ff7a9c6-ae75-4ccd-f066-005f77c819c7"},"source":["# As usual, a bit of setup\n","import time\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from cs231n.classifiers.fc_net import *\n","from cs231n.data_utils import get_CIFAR10_data\n","from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n","from cs231n.solver import Solver\n","\n","%matplotlib inline\n","plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n","plt.rcParams['image.interpolation'] = 'nearest'\n","plt.rcParams['image.cmap'] = 'gray'\n","\n","# for auto-reloading external modules\n","# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n","%load_ext autoreload\n","%autoreload 2\n","\n","def rel_error(x, y):\n","    \"\"\" returns relative error \"\"\"\n","    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n","\n","def print_mean_std(x,axis=0):\n","    print('  means: ', x.mean(axis=axis))\n","    print('  stds:  ', x.std(axis=axis))\n","    print() "],"execution_count":null,"outputs":[{"output_type":"stream","text":["=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n","\tYou will need to compile a Cython extension for a portion of this assignment.\n","\tThe instructions to do this will be given in a section of the notebook below.\n","\tThere will be an option for Colab users and another for Jupyter (local) users.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"tags":["pdf-ignore"],"colab":{"base_uri":"https://localhost:8080/"},"id":"MBzcjwfc6aAD","executionInfo":{"status":"ok","timestamp":1612695202579,"user_tz":-60,"elapsed":9106,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"c00812ed-64b2-4e82-936f-10eea6608d1c"},"source":["# Load the (preprocessed) CIFAR10 data.\n","data = get_CIFAR10_data()\n","for k, v in data.items():\n","  print('%s: ' % k, v.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X_train:  (49000, 3, 32, 32)\n","y_train:  (49000,)\n","X_val:  (1000, 3, 32, 32)\n","y_val:  (1000,)\n","X_test:  (1000, 3, 32, 32)\n","y_test:  (1000,)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"x5AHV8Td6aAD"},"source":["## Batch normalization: forward\n","In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n","\n","Referencing the paper linked to above in [1] may be helpful!"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R_8S9sUP6aAE","executionInfo":{"status":"ok","timestamp":1612695202581,"user_tz":-60,"elapsed":6986,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"b1a5a96b-4c5d-4278-e580-0c8291f4bf61"},"source":["# Check the training-time forward pass by checking means and variances\n","# of features both before and after batch normalization   \n","\n","# Simulate the forward pass for a two-layer network\n","np.random.seed(231)\n","N, D1, D2, D3 = 200, 50, 60, 3\n","X = np.random.randn(N, D1)\n","W1 = np.random.randn(D1, D2)\n","W2 = np.random.randn(D2, D3)\n","a = np.maximum(0, X.dot(W1)).dot(W2)\n","\n","print('Before batch normalization:')\n","print_mean_std(a,axis=0)\n","\n","gamma = np.ones((D3,))\n","beta = np.zeros((D3,))\n","# Means should be close to zero and stds close to one\n","print('After batch normalization (gamma=1, beta=0)')\n","a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n","print_mean_std(a_norm,axis=0)\n","\n","gamma = np.asarray([1.0, 2.0, 3.0])\n","beta = np.asarray([11.0, 12.0, 13.0])\n","# Now means should be close to beta and stds close to gamma\n","print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n","a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n","print_mean_std(a_norm,axis=0)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Before batch normalization:\n","  means:  [ -2.3814598  -13.18038246   1.91780462]\n","  stds:   [27.18502186 34.21455511 37.68611762]\n","\n","After batch normalization (gamma=1, beta=0)\n","  means:  [5.99520433e-17 6.93889390e-17 8.32667268e-19]\n","  stds:   [0.99999999 1.         1.        ]\n","\n","After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n","  means:  [11. 12. 13.]\n","  stds:   [0.99999999 1.99999999 2.99999999]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vA1u_NWd6aAE","executionInfo":{"status":"ok","timestamp":1612695203673,"user_tz":-60,"elapsed":1084,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"a8a44b87-f090-44cd-9c96-90332214e6a6"},"source":["# Check the test-time forward pass by running the training-time\n","# forward pass many times to warm up the running averages, and then\n","# checking the means and variances of activations after a test-time\n","# forward pass.\n","\n","np.random.seed(231)\n","N, D1, D2, D3 = 200, 50, 60, 3\n","W1 = np.random.randn(D1, D2)\n","W2 = np.random.randn(D2, D3)\n","\n","bn_param = {'mode': 'train'}\n","gamma = np.ones(D3)\n","beta = np.zeros(D3)\n","\n","for t in range(50):\n","  X = np.random.randn(N, D1)\n","  a = np.maximum(0, X.dot(W1)).dot(W2)\n","  batchnorm_forward(a, gamma, beta, bn_param)\n","\n","bn_param['mode'] = 'test'\n","X = np.random.randn(N, D1)\n","a = np.maximum(0, X.dot(W1)).dot(W2)\n","a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n","\n","# Means should be close to zero and stds close to one, but will be\n","# noisier than training-time forward passes.\n","print('After batch normalization (test-time):')\n","print_mean_std(a_norm,axis=0)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["After batch normalization (test-time):\n","  means:  [-0.03927353 -0.04349151 -0.10452686]\n","  stds:   [1.01531399 1.01238345 0.97819961]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"eA8vI-wL6aAE"},"source":["## Batch normalization: backward\n","Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n","\n","To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n","\n","Once you have finished, run the following to numerically check your backward pass."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZwGznLeJ6aAF","executionInfo":{"status":"ok","timestamp":1612695203675,"user_tz":-60,"elapsed":833,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"5cecc401-6b3f-4a0f-bd34-db0c60247aab"},"source":["# Gradient check batchnorm backward pass\n","np.random.seed(231)\n","N, D = 4, 5\n","x = 5 * np.random.randn(N, D) + 12\n","gamma = np.random.randn(D)\n","beta = np.random.randn(D)\n","dout = np.random.randn(N, D)\n","\n","bn_param = {'mode': 'train'}\n","fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n","fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n","fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n","\n","dx_num = eval_numerical_gradient_array(fx, x, dout)\n","da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n","db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n","\n","_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n","dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n","#You should expect to see relative errors between 1e-13 and 1e-8\n","print('dx error: ', rel_error(dx_num, dx))\n","print('dgamma error: ', rel_error(da_num, dgamma))\n","print('dbeta error: ', rel_error(db_num, dbeta))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dx error:  1.6674604875341426e-09\n","dgamma error:  7.417225040694815e-13\n","dbeta error:  2.379446949959628e-12\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"iL4OAHgK6aAG"},"source":["## Batch normalization: alternative backward\n","In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n","\n","Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n","\n","In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n","\n","we first calculate the mean $\\mu$ and variance $v$.\n","With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n","The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n","\n","\\begin{align}\n","& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n","& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n","\\end{align}"]},{"cell_type":"markdown","metadata":{"id":"F7siaU7F6aAH"},"source":["<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/batchnorm_graph.png\">"]},{"cell_type":"markdown","metadata":{"tags":["pdf-ignore"],"id":"kwqO7IDb6aAH"},"source":["The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n","\n","The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n","$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n","$\\frac{\\partial \\sigma}{\\partial v}$, \n","$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n","and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n","\n","If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n","\n","You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n","\n","After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G6JnoYE36aAI","executionInfo":{"status":"ok","timestamp":1612695211173,"user_tz":-60,"elapsed":1003,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"cbfa9c60-752f-4c89-9975-4495f1b58744"},"source":["np.random.seed(231)\n","N, D = 100, 500\n","x = 5 * np.random.randn(N, D) + 12\n","gamma = np.random.randn(D)\n","beta = np.random.randn(D)\n","dout = np.random.randn(N, D)\n","\n","bn_param = {'mode': 'train'}\n","out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n","\n","t1 = time.time()\n","dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n","t2 = time.time()\n","dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n","t3 = time.time()\n","\n","print('dx difference: ', rel_error(dx1, dx2))\n","print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n","print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n","print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dx difference:  9.890497291190823e-13\n","dgamma difference:  0.0\n","dbeta difference:  0.0\n","speedup: 1.21x\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"v21tz-rh6aAJ"},"source":["## Fully Connected Nets with Batch Normalization\n","Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n","\n","Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n","\n","HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."]},{"cell_type":"code","metadata":{"id":"L9WKxsZv6aAJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612698160689,"user_tz":-60,"elapsed":3549,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"d33c2cf3-ba6c-4823-855f-01301d5c377f"},"source":["np.random.seed(231)\n","N, D, H1, H2, C = 2, 15, 20, 30, 10\n","X = np.random.randn(N, D)\n","y = np.random.randint(C, size=(N,))\n","\n","# You should expect losses between 1e-4~1e-10 for W, \n","# losses between 1e-08~1e-10 for b,\n","# and losses between 1e-08~1e-09 for beta and gammas.\n","for reg in [0, 3.14]:\n","  print('Running check with reg = ', reg)\n","  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n","                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n","                            normalization='batchnorm')\n","\n","  loss, grads = model.loss(X, y)\n","  print('Initial loss: ', loss)\n","\n","  for name in sorted(grads):\n","    f = lambda _: model.loss(X, y)[0]\n","    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n","    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n","  if reg == 0: print()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Running check with reg =  0\n","Initial loss:  2.2611955101340957\n","W1 relative error: 1.10e-04\n","W2 relative error: 3.11e-06\n","W3 relative error: 4.05e-10\n","b1 relative error: 2.66e-07\n","b2 relative error: 2.72e-07\n","b3 relative error: 1.01e-10\n","beta1 relative error: 7.33e-09\n","beta2 relative error: 1.89e-09\n","gamma1 relative error: 6.96e-09\n","gamma2 relative error: 2.41e-09\n","\n","Running check with reg =  3.14\n","Initial loss:  6.996533220108303\n","W1 relative error: 1.98e-06\n","W2 relative error: 2.29e-06\n","W3 relative error: 2.79e-08\n","b1 relative error: 1.38e-08\n","b2 relative error: 7.99e-07\n","b3 relative error: 2.10e-10\n","beta1 relative error: 6.65e-09\n","beta2 relative error: 3.39e-09\n","gamma1 relative error: 6.27e-09\n","gamma2 relative error: 5.28e-09\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"XLUm_9Oa6aAJ"},"source":["# Batchnorm for deep networks\n","Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."]},{"cell_type":"code","metadata":{"id":"zMiOgAfx6aAK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612698172794,"user_tz":-60,"elapsed":10543,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"dfa87942-caba-4fee-957c-e35b5755d78f"},"source":["np.random.seed(231)\n","# Try training a very deep net with batchnorm\n","hidden_dims = [100, 100, 100, 100, 100]\n","\n","num_train = 1000\n","small_data = {\n","  'X_train': data['X_train'][:num_train],\n","  'y_train': data['y_train'][:num_train],\n","  'X_val': data['X_val'],\n","  'y_val': data['y_val'],\n","}\n","\n","weight_scale = 2e-2\n","bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n","model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n","\n","print('Solver with batch norm:')\n","bn_solver = Solver(bn_model, small_data,\n","                num_epochs=10, batch_size=50,\n","                update_rule='adam',\n","                optim_config={\n","                  'learning_rate': 1e-3,\n","                },\n","                verbose=True,print_every=20)\n","bn_solver.train()\n","\n","print('\\nSolver without batch norm:')\n","solver = Solver(model, small_data,\n","                num_epochs=10, batch_size=50,\n","                update_rule='adam',\n","                optim_config={\n","                  'learning_rate': 1e-3,\n","                },\n","                verbose=True, print_every=20)\n","solver.train()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Solver with batch norm:\n","(Iteration 1 / 200) loss: 2.297074\n","(Epoch 0 / 10) train acc: 0.102000; val_acc: 0.107000\n","(Epoch 1 / 10) train acc: 0.333000; val_acc: 0.263000\n","(Iteration 21 / 200) loss: 1.968218\n","(Epoch 2 / 10) train acc: 0.420000; val_acc: 0.289000\n","(Iteration 41 / 200) loss: 1.783523\n","(Epoch 3 / 10) train acc: 0.492000; val_acc: 0.309000\n","(Iteration 61 / 200) loss: 1.651492\n","(Epoch 4 / 10) train acc: 0.479000; val_acc: 0.302000\n","(Iteration 81 / 200) loss: 1.417491\n","(Epoch 5 / 10) train acc: 0.585000; val_acc: 0.327000\n","(Iteration 101 / 200) loss: 1.453174\n","(Epoch 6 / 10) train acc: 0.637000; val_acc: 0.340000\n","(Iteration 121 / 200) loss: 1.096966\n","(Epoch 7 / 10) train acc: 0.671000; val_acc: 0.330000\n","(Iteration 141 / 200) loss: 1.147829\n","(Epoch 8 / 10) train acc: 0.716000; val_acc: 0.328000\n","(Iteration 161 / 200) loss: 0.813335\n","(Epoch 9 / 10) train acc: 0.784000; val_acc: 0.330000\n","(Iteration 181 / 200) loss: 0.809768\n","(Epoch 10 / 10) train acc: 0.818000; val_acc: 0.321000\n","\n","Solver without batch norm:\n","(Iteration 1 / 200) loss: 2.302600\n","(Epoch 0 / 10) train acc: 0.124000; val_acc: 0.106000\n","(Epoch 1 / 10) train acc: 0.185000; val_acc: 0.168000\n","(Iteration 21 / 200) loss: 2.248002\n","(Epoch 2 / 10) train acc: 0.220000; val_acc: 0.226000\n","(Iteration 41 / 200) loss: 1.977625\n","(Epoch 3 / 10) train acc: 0.257000; val_acc: 0.218000\n","(Iteration 61 / 200) loss: 2.037953\n","(Epoch 4 / 10) train acc: 0.244000; val_acc: 0.227000\n","(Iteration 81 / 200) loss: 2.000312\n","(Epoch 5 / 10) train acc: 0.299000; val_acc: 0.237000\n","(Iteration 101 / 200) loss: 1.748184\n","(Epoch 6 / 10) train acc: 0.362000; val_acc: 0.279000\n","(Iteration 121 / 200) loss: 1.724046\n","(Epoch 7 / 10) train acc: 0.342000; val_acc: 0.258000\n","(Iteration 141 / 200) loss: 1.696783\n","(Epoch 8 / 10) train acc: 0.366000; val_acc: 0.252000\n","(Iteration 161 / 200) loss: 1.539274\n","(Epoch 9 / 10) train acc: 0.426000; val_acc: 0.256000\n","(Iteration 181 / 200) loss: 1.462405\n","(Epoch 10 / 10) train acc: 0.404000; val_acc: 0.273000\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"t0nXoq006aAK"},"source":["Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."]},{"cell_type":"code","metadata":{"tags":["pdf-ignore-input"],"id":"k_vQmi1Q6aAK","colab":{"base_uri":"https://localhost:8080/","height":893},"executionInfo":{"status":"ok","timestamp":1612699453052,"user_tz":-60,"elapsed":1663,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"66db6514-2035-47e4-f7fb-5f37828912f7"},"source":["def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n","    \"\"\"utility function for plotting training history\"\"\"\n","    plt.title(title)\n","    plt.xlabel(label)\n","    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n","    bl_plot = plot_fn(baseline)\n","    num_bn = len(bn_plots)\n","    for i in range(num_bn):\n","        label='with_norm'\n","        if labels is not None:\n","            label += str(labels[i])\n","        plt.plot(bn_plots[i], bn_marker, label=label)\n","    label='baseline'\n","    if labels is not None:\n","        label += str(labels[0])\n","    plt.plot(bl_plot, bl_marker, label=label)\n","    plt.legend(loc='lower center', ncol=num_bn+1) \n","\n","    \n","plt.subplot(3, 1, 1)\n","plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n","                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n","plt.subplot(3, 1, 2)\n","plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n","                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n","plt.subplot(3, 1, 3)\n","plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n","                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n","\n","plt.gcf().set_size_inches(15, 15)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x1080 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"NfTxCC306aAK"},"source":["# Batch normalization and initialization\n","We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n","\n","The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."]},{"cell_type":"code","metadata":{"tags":["pdf-ignore-input"],"id":"MtCy2jap6aAK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612699734151,"user_tz":-60,"elapsed":193571,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"fdf5d8ec-8e24-454c-a44f-3aa31bf0cdf6"},"source":["np.random.seed(231)\n","# Try training a very deep net with batchnorm\n","hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n","num_train = 1000\n","small_data = {\n","  'X_train': data['X_train'][:num_train],\n","  'y_train': data['y_train'][:num_train],\n","  'X_val': data['X_val'],\n","  'y_val': data['y_val'],\n","}\n","\n","bn_solvers_ws = {}\n","solvers_ws = {}\n","weight_scales = np.logspace(-4, 0, num=20)\n","for i, weight_scale in enumerate(weight_scales):\n","    print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n","    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n","    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n","\n","    bn_solver = Solver(bn_model, small_data,\n","                  num_epochs=10, batch_size=50,\n","                  update_rule='adam',\n","                  optim_config={\n","                    'learning_rate': 1e-3,\n","                  },\n","                  verbose=False, print_every=200)\n","    bn_solver.train()\n","    bn_solvers_ws[weight_scale] = bn_solver\n","\n","    solver = Solver(model, small_data,\n","                  num_epochs=10, batch_size=50,\n","                  update_rule='adam',\n","                  optim_config={\n","                    'learning_rate': 1e-3,\n","                  },\n","                  verbose=False, print_every=200)\n","    solver.train()\n","    solvers_ws[weight_scale] = solver"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Running weight scale 1 / 20\n","Running weight scale 2 / 20\n","Running weight scale 3 / 20\n","Running weight scale 4 / 20\n","Running weight scale 5 / 20\n","Running weight scale 6 / 20\n","Running weight scale 7 / 20\n","Running weight scale 8 / 20\n","Running weight scale 9 / 20\n","Running weight scale 10 / 20\n","Running weight scale 11 / 20\n","Running weight scale 12 / 20\n","Running weight scale 13 / 20\n","Running weight scale 14 / 20\n","Running weight scale 15 / 20\n","Running weight scale 16 / 20\n","Running weight scale 17 / 20\n","Running weight scale 18 / 20\n","Running weight scale 19 / 20\n","Running weight scale 20 / 20\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"tags":["pdf-ignore-input"],"id":"VjJ7bWx56aAL","colab":{"base_uri":"https://localhost:8080/","height":897},"executionInfo":{"status":"ok","timestamp":1612699738758,"user_tz":-60,"elapsed":2345,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"f9a7cf6b-f576-4bdc-afb5-69574e5d8e7c"},"source":["# Plot results of weight scale experiment\n","best_train_accs, bn_best_train_accs = [], []\n","best_val_accs, bn_best_val_accs = [], []\n","final_train_loss, bn_final_train_loss = [], []\n","\n","for ws in weight_scales:\n","  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n","  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n","  \n","  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n","  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n","  \n","  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n","  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n","  \n","plt.subplot(3, 1, 1)\n","plt.title('Best val accuracy vs weight initialization scale')\n","plt.xlabel('Weight initialization scale')\n","plt.ylabel('Best val accuracy')\n","plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n","plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n","plt.legend(ncol=2, loc='lower right')\n","\n","plt.subplot(3, 1, 2)\n","plt.title('Best train accuracy vs weight initialization scale')\n","plt.xlabel('Weight initialization scale')\n","plt.ylabel('Best training accuracy')\n","plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n","plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n","plt.legend()\n","\n","plt.subplot(3, 1, 3)\n","plt.title('Final training loss vs weight initialization scale')\n","plt.xlabel('Weight initialization scale')\n","plt.ylabel('Final training loss')\n","plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n","plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n","plt.legend()\n","plt.gca().set_ylim(1.0, 3.5)\n","\n","plt.gcf().set_size_inches(15, 15)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x1080 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"tags":["pdf-inline"],"id":"CD57P6rO6aAL"},"source":["## Inline Question 1:\n","Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n","\n","## Answer:\n","- Although neural nets using batch normalization performs globally better (in terms of accuracy) regarding the nets without batchnorm, we notice that the batchnorm is clearly affected by the initialization scale. In my opinion, this is due to the fact that, for all initialization scale weights, the networks were trained within the same number of epochs, and it's clear that the batchnorm layer needs more time to learn *scale* and *shift* parameters for high initialization scales.\n","- We notice also that using batchnorm prevents loss explosion with high initialization scale (3rd graph above)."]},{"cell_type":"markdown","metadata":{"id":"rIapjFeA6aAL"},"source":["# Batch normalization and batch size\n","We will now run a small experiment to study the interaction of batch normalization and batch size.\n","\n","The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."]},{"cell_type":"code","metadata":{"tags":["pdf-ignore-input"],"id":"1db7yqeC6aAL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612699958333,"user_tz":-60,"elapsed":77572,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"cb80c65d-a99d-4713-f5c0-9f8f5a89ed45"},"source":["def run_batchsize_experiments(normalization_mode):\n","    np.random.seed(231)\n","    # Try training a very deep net with batchnorm\n","    hidden_dims = [100, 100, 100, 100, 100]\n","    num_train = 1000\n","    small_data = {\n","      'X_train': data['X_train'][:num_train],\n","      'y_train': data['y_train'][:num_train],\n","      'X_val': data['X_val'],\n","      'y_val': data['y_val'],\n","    }\n","    n_epochs=10\n","    weight_scale = 2e-2\n","    batch_sizes = [5,10,50]\n","    lr = 10**(-3.5)\n","    solver_bsize = batch_sizes[0]\n","\n","    print('No normalization: batch size = ',solver_bsize)\n","    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n","    solver = Solver(model, small_data,\n","                    num_epochs=n_epochs, batch_size=solver_bsize,\n","                    update_rule='adam',\n","                    optim_config={\n","                      'learning_rate': lr,\n","                    },\n","                    verbose=False)\n","    solver.train()\n","    \n","    bn_solvers = []\n","    for i in range(len(batch_sizes)):\n","        b_size=batch_sizes[i]\n","        print('Normalization: batch size = ',b_size)\n","        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n","        bn_solver = Solver(bn_model, small_data,\n","                        num_epochs=n_epochs, batch_size=b_size,\n","                        update_rule='adam',\n","                        optim_config={\n","                          'learning_rate': lr,\n","                        },\n","                        verbose=False)\n","        bn_solver.train()\n","        bn_solvers.append(bn_solver)\n","        \n","    return bn_solvers, solver, batch_sizes\n","\n","batch_sizes = [5,10,50]\n","bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["No normalization: batch size =  5\n","Normalization: batch size =  5\n","Normalization: batch size =  10\n","Normalization: batch size =  50\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"uX7qGqJV6aAL","colab":{"base_uri":"https://localhost:8080/","height":482},"executionInfo":{"status":"ok","timestamp":1612699973690,"user_tz":-60,"elapsed":1949,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"bf72cfc6-6497-4cc5-8d5e-75c29ae5e979"},"source":["plt.subplot(2, 1, 1)\n","plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n","                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n","plt.subplot(2, 1, 2)\n","plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n","                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n","\n","plt.gcf().set_size_inches(15, 10)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"tags":["pdf-inline"],"id":"lnRRJ4SU6aAM"},"source":["## Inline Question 2:\n","Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n","\n","## Answer:\n","We notice that batchnorm is highly dependent on batch size used during training. This is due to the fact that the batchnorm normalization step are performed per feature (i.e. vertically), increasing the batch size will result in more accurate normalization step. As a result, we conclude that: In batchnorm, the higher the batch size, the better."]},{"cell_type":"markdown","metadata":{"id":"D8q7JNg16aAM"},"source":["# Layer Normalization\n","Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n","\n","Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n","\n","[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"]},{"cell_type":"markdown","metadata":{"tags":["pdf-inline"],"id":"x4QugiRw6aAM"},"source":["## Inline Question 3:\n","Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n","\n","1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n","2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n","3. Subtracting the mean image of the dataset from each image in the dataset.\n","4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n","\n","## Answer:\n","- Data preprocessing step that is analogous to the **batch** normalization: **3** (In scale and shift step, put $\\gamma = \\sigma$ and $\\beta = 0$. This will result to: $x \\leftarrow x - \\mu$).\n","- Data preprocessing step that is analogous to the **layer** normalization: **2** (put: batch size = #samples, $\\mu = 0$ and $\\beta = 0$. This will result to: $x \\leftarrow \\frac{x^2}{\\sum x^2}$)."]},{"cell_type":"markdown","metadata":{"id":"nO5GubmJ6aAM"},"source":["# Layer Normalization: Implementation\n","\n","Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n","\n","Here's what you need to do:\n","\n","* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_forward`. \n","\n","Run the cell below to check your results.\n","* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n","\n","Run the second cell below to check your results.\n","* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n","\n","Run the third cell below to run the batch size experiment on layer normalization."]},{"cell_type":"code","metadata":{"id":"C4Rfxn1i6aAM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612701972989,"user_tz":-60,"elapsed":686,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"076fed46-c926-45e4-cf68-217d26b2eabc"},"source":["# Check the training-time forward pass by checking means and variances\n","# of features both before and after layer normalization   \n","\n","# Simulate the forward pass for a two-layer network\n","np.random.seed(231)\n","N, D1, D2, D3 =4, 50, 60, 3\n","X = np.random.randn(N, D1)\n","W1 = np.random.randn(D1, D2)\n","W2 = np.random.randn(D2, D3)\n","a = np.maximum(0, X.dot(W1)).dot(W2)\n","\n","print('Before layer normalization:')\n","print_mean_std(a,axis=1)\n","\n","gamma = np.ones(D3)\n","beta = np.zeros(D3)\n","# Means should be close to zero and stds close to one\n","print('After layer normalization (gamma=1, beta=0)')\n","a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n","print_mean_std(a_norm,axis=1)\n","\n","gamma = np.asarray([3.0,3.0,3.0])\n","beta = np.asarray([5.0,5.0,5.0])\n","# Now means should be close to beta and stds close to gamma\n","print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n","a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n","print_mean_std(a_norm,axis=1)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Before layer normalization:\n","  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n","  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n","\n","After layer normalization (gamma=1, beta=0)\n","  means:  [ 4.81096644e-16 -7.40148683e-17  2.22044605e-16 -5.92118946e-16]\n","  stds:   [0.99999995 0.99999999 1.         0.99999969]\n","\n","After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n","  means:  [5. 5. 5. 5.]\n","  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_lTGIX-K6aAN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1612703033065,"user_tz":-60,"elapsed":871,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"3083ab74-b5a7-47fe-a190-f43205b59a19"},"source":["# Gradient check batchnorm backward pass\n","np.random.seed(231)\n","N, D = 4, 5\n","x = 5 * np.random.randn(N, D) + 12\n","gamma = np.random.randn(D)\n","beta = np.random.randn(D)\n","dout = np.random.randn(N, D)\n","\n","ln_param = {}\n","fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n","fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n","fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n","\n","dx_num = eval_numerical_gradient_array(fx, x, dout)\n","da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n","db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n","\n","_, cache = layernorm_forward(x, gamma, beta, ln_param)\n","dx, dgamma, dbeta = layernorm_backward(dout, cache)\n","\n","#You should expect to see relative errors between 1e-12 and 1e-8\n","print('dx error: ', rel_error(dx_num, dx))\n","print('dgamma error: ', rel_error(da_num, dgamma))\n","print('dbeta error: ', rel_error(db_num, dbeta))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dx error:  2.107279036881856e-09\n","dgamma error:  4.519489546032799e-12\n","dbeta error:  2.5842537629899423e-12\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"p5HA17-y6aAN"},"source":["# Layer Normalization and batch size\n","\n","We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"]},{"cell_type":"code","metadata":{"id":"HtJMOAkU6aAN","colab":{"base_uri":"https://localhost:8080/","height":689},"executionInfo":{"status":"ok","timestamp":1612703750024,"user_tz":-60,"elapsed":77290,"user":{"displayName":"Soufian Benamara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj7-UwGIuLvBwNdi62Q2pMRjoHrgtEm3LZkyGEWZw=s64","userId":"08596286217915945464"}},"outputId":"a18ccd83-c27e-446e-dd28-5caaa3833756"},"source":["ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n","\n","plt.subplot(2, 1, 1)\n","plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n","                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n","plt.subplot(2, 1, 2)\n","plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n","                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n","\n","plt.gcf().set_size_inches(15, 10)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["No normalization: batch size =  5\n","Normalization: batch size =  5\n","Normalization: batch size =  10\n","Normalization: batch size =  50\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x720 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"tags":["pdf-inline"],"id":"RAGHQM4s6aAN"},"source":["## Inline Question 4:\n","When is layer normalization likely to not work well, and why?\n","\n","1. Using it in a very deep network\n","2. Having a very small dimension of features\n","3. Having a high regularization term\n","\n","\n","## Answer:\n","- Layer normalization is likely to not work well in **(2)**. This is due to the fact that having a small number of features will lead to inaccurate normalizing step (subtract the mean, and divide by std), i.e. normalizing throughout the features will be wrong, caused by the lack of sufficient number of features.\n","\n","- This case is similar to having a small batch size in batch normalization."]}]}